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Manko, H.

Publications and source records attributed to Manko, H..

2 recordsLinked to original sources

PvdL Orchestrates the Assembly of the Nonribosomal Peptide Synthetases Involved in Pyoverdine Biosynthesis in Pseudomonas aeruginosa.

The pyoverdine siderophore is produced by Pseudomonas aeruginosa to access iron. Its synthesis involves the complex co-ordination of four nonribosomal peptide synthetases (NRPSs), responsible for assembling the pyoverdine peptide backbone. The precise cellular organization of these NRPSs and their mechanisms of interaction remain unclear. Here, we used a combination of several single-molecule microscopy techniques to to elucidate the spatial arrangement of NRPSs within pyoverdine-producing cells. Our findings revealed that PvdL differs from the three other NRPS in term of localization and mobility patterns. PvdL is predominantly located at the inner membrane, while the other also explore the cytoplasmic compartment. Leveraging the power of multicolor single-molecule localization, we further reveal co-localization between PvdL and the other NRPSs, suggesting a pivotal role for PvdL in orchestrating the intricate biosynthetic pathway. Our observations strongly indicates that PvdL serves as a central orchestrator in the assembly of NRPSs involved in pyoverdine biosynthesis, assuming a critical regulatory function.

microbiology↗

Advancing Spectrally-Resolved Single Molecule Localization Microscopy using Deep Learning

Spectrally-Resolved Single Molecule Localization Microscopy (srSMLM) is a recent multidimensional technique enriching single molecule localization imaging by the simultaneous recording of single emitters spectra. As for SMLM, the localization precision is fundamentally limited by the number of photons collected per emitters. But srSMLM is more impacted because splitting the emission light from single emitters into a spatial and a spectral channel further reduces the number of photons available for each channel and impairs both spatial and spectral precision - or forces the sacrifice of one or the other. Here, we explored the potential of deep learning to overcome this limitation. We report srUnet - a Unet-based image processing that enhances the spectral and spatial signals and compensates for the signal loss inherent in recording the spectral component. We showed that localization and spectral precision of low-emitting species remain as good as those obtained with a high photons budget together with improving the fraction of localizations whose signal is both spatially and spectrally interpretable. srUnet is able to deal with spectral shift and its application to multicolour imaging in biological sample is straight-forward. srUnet advances spectrally resolved single molecule localization microscopy to achieve performance close to conventional SMLM without complicating its use.

biophysics↗